dds_project_2
Jake
2023-04-09
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## layout## Warning: package 'smotefamily' was built under R version 4.2.3## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## AttritionNo AttritionYes
## YearsSinceLastPromotion 0.004573321 -0.004573321
## JobRoleSales.Executive -0.006067091 0.006067091
## EducationFieldOther -0.008341784 0.008341784
## PercentSalaryHike -0.015328069 0.015328069
## PerformanceRating -0.015333837 0.015333837
## EmployeeNumber 0.022793834 -0.022793834
## GenderMale -0.025250336 0.025250336
## EducationFieldLife.Sciences 0.029298301 -0.029298301
## JobRoleHuman.Resources -0.029856670 0.029856670
## DailyRate 0.033793125 -0.033793125
## JobRoleResearch.Scientist -0.033944582 0.033944582
## HourlyRate -0.036554178 0.036554178
## BusinessTravelTravel_Rarely 0.037571487 -0.037571487
## EducationFieldMarketing -0.038327289 0.038327289
## RelationshipSatisfaction 0.039646611 -0.039646611
## MonthlyRate 0.043232173 -0.043232173
## EducationFieldMedical 0.043599339 -0.043599339
## JobRoleLaboratory.Technician -0.044199272 0.044199272
## ID -0.047266995 0.047266995
## Education 0.049442357 -0.049442357
## MaritalStatusMarried 0.049987703 -0.049987703
## EducationFieldTechnical.Degree -0.054956321 0.054956321
## JobRoleManager 0.056018129 -0.056018129
## NumCompaniesWorked -0.061018887 0.061018887
## TrainingTimesLastYear 0.062726088 -0.062726088
## EnvironmentSatisfaction 0.077325405 -0.077325405
## BusinessTravelTravel_Frequently -0.077687476 0.077687476
## DistanceFromHome -0.087136293 0.087136293
## WorkLifeBalance 0.089789709 -0.089789709
## JobRoleResearch.Director 0.095965483 -0.095965483
## DepartmentResearch...Development 0.100973416 -0.100973416
## DepartmentSales -0.101580128 0.101580128
## JobSatisfaction 0.107520935 -0.107520935
## JobRoleManufacturing.Director 0.125122249 -0.125122249
## YearsAtCompany 0.128754060 -0.128754060
## YearsWithCurrManager 0.146782245 -0.146782245
## StockOptionLevel 0.148680303 -0.148680303
## Age 0.149383577 -0.149383577
## MonthlyIncome 0.154914955 -0.154914955
## YearsInCurrentRole 0.156215707 -0.156215707
## JobLevel 0.162136444 -0.162136444
## TotalWorkingYears 0.167206122 -0.167206122
## MaritalStatusSingle -0.180799531 0.180799531
## JobInvolvement 0.187793409 -0.187793409
## JobRoleSales.Representative -0.202334830 0.202334830
## OverTimeYes -0.272036591 0.272036591
## AttritionNo 1.000000000 -1.000000000
## AttritionYes -1.000000000 1.000000000
## Warning in `[<-.data.frame`(`*tmp*`,
## sapply(attrition_factors_data_with_enginering, : provided 8 variables to replace
## 1 variables
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels## K_value Accuracy Sensitivity Specificity
## 1 1 0.8401725 0.8219470 0.8595283
## 2 2 0.8294219 0.8060421 0.8542308
## 3 3 0.8445874 0.8012279 0.8903894
## 4 4 0.8366713 0.7963706 0.8792546
## 5 5 0.8363357 0.7971915 0.8777393
## 6 6 0.8277902 0.7942255 0.8633724
## 7 7 0.8254452 0.7930795 0.8598395
## 8 8 0.8215524 0.7926925 0.8523449
## 9 9 0.8207133 0.7948512 0.8483790
## 10 10 0.8172960 0.7950093 0.8412495
## [1] "Mean of Accuracy"## [1] 0.7030303## [1] "Mean of Sensitivity"## [1] 0.6530796## [1] "Mean of Specificity"## [1] 0.755993##
## Call:
## lm(formula = MonthlyIncome ~ MonthlyRate + StockOptionLevel +
## YearsAtCompany + TotalWorkingYears + YearsSinceLastPromotion +
## YearsWithCurrManager + JobInvolvement + Age + JobLevel +
## JobRole + Gender + Education + Department + DailyRate + BusinessTravel +
## HourlyRate, data = salary_factors_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3694.3 -655.4 -23.8 628.1 4022.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.286e+02 5.747e+02 -0.920 0.357971
## MonthlyRate -9.349e-03 5.080e-03 -1.840 0.066067 .
## StockOptionLevel -6.440e-01 4.205e+01 -0.015 0.987784
## YearsAtCompany -1.978e+00 1.177e+01 -0.168 0.866549
## TotalWorkingYears 5.028e+01 1.032e+01 4.870 1.33e-06 ***
## YearsSinceLastPromotion 3.031e+01 1.490e+01 2.034 0.042286 *
## YearsWithCurrManager -2.594e+01 1.593e+01 -1.628 0.103889
## JobInvolvement 9.083e+00 5.181e+01 0.175 0.860873
## Age -1.448e-01 5.538e+00 -0.026 0.979151
## JobLevel 2.782e+03 8.226e+01 33.819 < 2e-16 ***
## JobRoleHuman Resources -1.828e+02 5.082e+02 -0.360 0.719109
## JobRoleLaboratory Technician -6.117e+02 1.689e+02 -3.621 0.000312 ***
## JobRoleManager 4.260e+03 2.793e+02 15.252 < 2e-16 ***
## JobRoleManufacturing Director 1.580e+02 1.668e+02 0.947 0.343934
## JobRoleResearch Director 4.059e+03 2.164e+02 18.758 < 2e-16 ***
## JobRoleResearch Scientist -3.446e+02 1.685e+02 -2.045 0.041208 *
## JobRoleSales Executive 4.826e+02 3.545e+02 1.362 0.173682
## JobRoleSales Representative 8.835e+01 3.873e+02 0.228 0.819626
## GenderMale 1.171e+02 7.350e+01 1.594 0.111387
## Education -3.221e+01 3.642e+01 -0.884 0.376808
## DepartmentResearch & Development 1.981e+02 4.357e+02 0.455 0.649440
## DepartmentSales -3.301e+02 4.428e+02 -0.745 0.456282
## DailyRate 1.611e-01 9.003e-02 1.790 0.073823 .
## BusinessTravelTravel_Frequently 2.140e+02 1.392e+02 1.537 0.124743
## BusinessTravelTravel_Rarely 4.035e+02 1.178e+02 3.427 0.000640 ***
## HourlyRate -5.330e-01 1.792e+00 -0.297 0.766204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1052 on 844 degrees of freedom
## Multiple R-squared: 0.9492, Adjusted R-squared: 0.9477
## F-statistic: 630.5 on 25 and 844 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) -1.656591e+03 5.994367e+02
## MonthlyRate -1.931930e-02 6.218751e-04
## StockOptionLevel -8.317761e+01 8.188955e+01
## YearsAtCompany -2.507085e+01 2.111540e+01
## TotalWorkingYears 3.001777e+01 7.054325e+01
## YearsSinceLastPromotion 1.058242e+00 5.956735e+01
## YearsWithCurrManager -5.721457e+01 5.333552e+00
## JobInvolvement -9.260771e+01 1.107738e+02
## Age -1.101455e+01 1.072502e+01
## JobLevel 2.620563e+03 2.943485e+03
## JobRoleHuman Resources -1.180302e+03 8.146377e+02
## JobRoleLaboratory Technician -9.432464e+02 -2.800609e+02
## JobRoleManager 3.711362e+03 4.807712e+03
## JobRoleManufacturing Director -1.694634e+02 4.854103e+02
## JobRoleResearch Director 3.634475e+03 4.483944e+03
## JobRoleResearch Scientist -6.753252e+02 -1.378249e+01
## JobRoleSales Executive -2.130829e+02 1.178341e+03
## JobRoleSales Representative -6.718999e+02 8.485994e+02
## GenderMale -2.712922e+01 2.613867e+02
## Education -1.036979e+02 3.928295e+01
## DepartmentResearch & Development -6.570940e+02 1.053338e+03
## DepartmentSales -1.199254e+03 5.391350e+02
## DailyRate -1.556113e-02 3.378571e-01
## BusinessTravelTravel_Frequently -5.932667e+01 4.872571e+02
## BusinessTravelTravel_Rarely 1.723973e+02 6.346866e+02
## HourlyRate -4.049967e+00 2.984035e+00## Linear Regression
##
## 870 samples
## 3 predictor
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 870, 870, 870, 870, 870, 870, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 1385.817 0.9082617 1053.879
##
## Tuning parameter 'intercept' was held constant at a value of TRUE## Linear Regression
##
## 870 samples
## 4 predictor
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 870, 870, 870, 870, 870, 870, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 1081.908 0.942816 823.5401
##
## Tuning parameter 'intercept' was held constant at a value of TRUE##
## Call:
## lm(formula = MonthlyIncome ~ TotalWorkingYears + JobLevel + JobRole +
## YearsSinceLastPromotion, data = salary_factors_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3930.6 -623.2 -13.1 620.7 4135.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -98.879 203.311 -0.486 0.626847
## TotalWorkingYears 45.109 8.388 5.378 9.71e-08 ***
## JobLevel 2792.476 81.819 34.130 < 2e-16 ***
## JobRoleHuman Resources -322.607 251.720 -1.282 0.200325
## JobRoleLaboratory Technician -595.578 168.976 -3.525 0.000446 ***
## JobRoleManager 4019.606 229.226 17.536 < 2e-16 ***
## JobRoleManufacturing Director 143.668 167.299 0.859 0.390719
## JobRoleResearch Director 4024.643 216.229 18.613 < 2e-16 ***
## JobRoleResearch Scientist -324.687 169.371 -1.917 0.055568 .
## JobRoleSales Executive -66.338 144.207 -0.460 0.645617
## JobRoleSales Representative -414.767 211.680 -1.959 0.050388 .
## YearsSinceLastPromotion 13.835 12.804 1.081 0.280212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1063 on 858 degrees of freedom
## Multiple R-squared: 0.9473, Adjusted R-squared: 0.9466
## F-statistic: 1401 on 11 and 858 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) -497.92427 300.1658315
## TotalWorkingYears 28.64659 61.5719899
## JobLevel 2631.88792 2953.0640115
## JobRoleHuman Resources -816.66532 171.4517983
## JobRoleLaboratory Technician -927.23265 -263.9241735
## JobRoleManager 3569.69571 4469.5159215
## JobRoleManufacturing Director -184.69516 472.0305855
## JobRoleResearch Director 3600.24367 4449.0426293
## JobRoleResearch Scientist -657.11679 7.7428346
## JobRoleSales Executive -349.37755 216.7010605
## JobRoleSales Representative -830.23775 0.7034615
## YearsSinceLastPromotion -11.29554 38.9650259x <- rnorm(50) y <- rnorm(50) z <- rnorm(50)
fig <- plot_ly(x = x, y = y, z = z, type = “scatter3d”, mode = “markers”) fig <- fig %>% add_markers(color = z, colors = “Blues”) fig <- fig %>% layout(scene = list(xaxis = list(title = “X”), yaxis = list(title = “Y”), zaxis = list(title = “Z”))) fig